Applied Deep Learning Implementation

This unit guides students through practical construction, optimization, and deployment of deep learning models using modern frameworks and tools. Students will learn to improve training and inference performance, defend against adversarial inputs, implement real-time and device-based applications, and address ethical, explainability, and support concerns in production systems.

Learning Objectives

• Analyze model training results by interpreting loss landscapes, probability outputs, and overfitting indicators to select and justify optimization and regularization strategies.
• Apply deep learning frameworks (e.g., TensorFlow, Chainer, Torch) to implement, profile, and optimize models for tasks such as image feature detection, sentiment analysis, language translation, and real-time behavior analysis.
• Demonstrate methods to detect and mitigate adversarial attacks, improve inference speed and scalability, and adapt models for deployment on embedded devices or production services.
• Evaluate deep learning system trade-offs and non-technical considerations by comparing deep learning to other AI approaches, addressing hype and ethical concerns, and designing customer support and maintenance plans for deployed applications.